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In the drug discovery pipeline, evaluating multiple pathways between a drug target and disease is crucial for identifying potential therapeutic approaches. By examining overlapping pathways between competing drugs, researchers can uncover novel drug targets, while isoform-specific findings may elucidate unexpected clinical trial outcomes. Filtering these pathways with real-world expression and proteomics data is essential to validate new hypotheses and avoid pursuing non-viable leads.

Leveraging tools such as Neo4j, Python, and R, powered by curated databases like the QIAGEN Biomedical Knowledge Base and OmicSoft Lands, enables scientists to efficiently explore potential mechanisms of action during both target discovery and later stages of drug development.

Attendees will learn to:

• Apply pathfinding algorithms to navigate our comprehensive knowledge graph.

• Qualify drug candidates using curated scRNA-Seq expression data and detailed cell type annotations.

• Expand, filter, prioritize, and refine lists of biomarkers and drug targets through various advanced approaches.

In this training, you will learn how to analyze and interpret your own single cell RNA-seq data using QIAGEN CLC Genomics Workbench starting with either FASTQ or matrix files.

Using CLC Genomics Workbench, you will learn how to perform secondary analysis on your single cell RNA-seq data. Specifically, you will learn how to:

• Import your raw FASTQ or processed cell-matrix files.

• Use pre-configured but customizable pipelines/workflows for single cell RNA-seq data.

• Generate high resolution visuals and other files from your analysis for publications and biopharmaceutical discoveries.

o Dimension reduction (UMAP, t-SNE) plots

o Differential expression table for clusters, cell types, or combination of both

o Heat map

o Dot plots

o Violin plots

• Learn how to use “Create Cell Annotations from Hashtags” for cell hashing (i.e., CITE-seq).

• Dive into spatial transcriptomic analysis, the latest feature in the single cell RNA-seq module.

While there is great interest in the scientific community to investigate drug targets and biomarkers from public immune-oncology data sources such as The Cancer Genome Atlas (TCGA), such investigation is hindered by difficulties in finding and combining related datasets to perform large-scale meta-analyses. This webinar will focus on how high-quality curated genomic repositories such as QIAGEN OmicSoft Lands immediately allows in-depth investigations across diverse data sources (GEO, CPTAC, TCGA, GTEx and more) to discover and validate candidate checkpoint inhibitor drug targets and biomarkers.

You will discover how to:

• Use sample IDs to create a custom dataset from the deeply curated TCGA collection available within OmicSoft Lands

• Easily download count information for each sample across all genes and create a local or server project

• Calculate differential expression using metadata of interest

• Generate volcano, PCA/PCOA, heatmap, expression and other plots for scientific discoveries

• Upload the data to Ingenuity Pathway Analysis for hypothesis generation

This training will be relevant to both QIAGEN Main and Genomics workbench users and prospects who are interested in below analytics.

• Alignment and tree construction

• Sanger sequencing analysis

• Cloning and primer design

• Other molecular biology tools

Are you new to QIAGEN Ingenuity Pathway Analysis (IPA) or interested in expanding your skill set? Join us as we learn more on large dataset analysis and knowledge base queries using QIAGEN IPA.

You’ll learn to:

• Upload multiple dataset types (e.g., RNA-seq, proteomics, metabolomics) and perform interactive core/pathway analysis in IPA

• Learn how to interpret different results, including pathways, key regulators, impact on biological functions/diseases and more

• Compare different experimental conditions (e.g., single-cell clusters, disease types) and identify similarities and contrasts

• Generate a network for hypothesis generation, even without a dataset or experimental design

Already have an IPA license? Install IPA and start using it now: https://qiagen.showpad.com/share/CBv30blCPKFDUYHRWtAvI

Learn more about IPA or request a free trial: https://digitalinsights.qiagen.com/ipa

There is a wealth of RNA-seq expression data scattered across public sources like GEO, SRA, TCGA and GTEx, but scientists interested in biomarker discovery need to devote significant time and resources to finding, cleaning and standardizing relevant data.

Start saving time with OmicSoft Lands, an accessible source of manually-curated, unified data available through both graphical user interface (GUI) and APIs.

We’ll cover how to:

- Easily find RNA-seq expression data of interest (specific diseases, drug treatments, tissues etc.) using the GUI and APIs

- Compare expression across customized conditions of interest

- Investigate the impact of normalization methods within and across projects

Not sure what to do with Regulator Effects and Interaction Networks in IPA? After you conduct an IPA Core Analysis on your ‘omic dataset (RNA-seq, scRNA-seq, proteomics, metabolomics and more), start using the Regulator Effects and Interaction Networks results to interpret your data and generate hypotheses.

We’ll cover:

- Regulator Effects: These causal and directional networks connect major regulators with downstream diseases and biological functions

- Interaction Networks: These networks focus on the interconnectivity of molecules (genes, proteins, metabolites etc.) in your dataset

Start out strong with QIAGEN CLC Genomics Workbench and follow along with our live demo of its popular features.

We’ll cover how to:

Install and set up your Workbench

Add plugins like Biomedical Genomics Analysis plugin

Manage data with Reference Data Management

Work with reads and using tools and workflows to batch process your data

Visualize data with Track List and Genome Browser

Export data and share workflows

If you have any questions, we’ll answer them during the webinar.

This event will feature four public health experts highlighting exciting collaborations within the PHL space across various workflows, including Antimicrobial Resistance Tracking, Vector-borne Disease Monitoring, Waterborne Pathogen Detection, and PulseNet Extraction/Sequencing. Come learn more about pathogen detection from the unseen heroes of public health!

The purpose of the QIAGEN Public Health Virtual Symposium is to inspire and equip public health professionals with the latest insights and innovative solutions to advance public health laboratory practices, fostering stronger collaborations and improving outcomes in public health initiatives.

Speakers:

Alex Rossheim, MS, Colorado Department of Public Health and Environment:

From Extraction to Sequencing: Evaluating QIAGEN Workflows for PulseNet Pathogens

Brenna McGruder, PhD, Florida Bureau of Public Health Labs - Jacksonville:

Disaster Patrol: Utilizing Digital PCR to Monitor Dangerous Pathogens Across Storm Cycles in Hurricane-Affected Regions

Becca Harrell, MLT, Tennessee Department of Health:

Enhancing Microbial Detection: Boosting Precision, Accuracy, and Sensitivity with Digital PCR for Pathogen Detection

Doris Di, PhD, Hawaii State Department of Health:

Flushing the Threat: Tracking Antimicrobial Resistance in Hawaiian Communities through Wastewater Based Epidemiology